As a craft brewing enthusiast with a multi-tap keezer system, I faced several interconnected challenges that were affecting both beer quality and operational efficiency. The existing manual approach to keezer management was creating friction in the user experience and wasting energy.
The Problem
Core User Pain Points
- Line Contamination: Switching between beer styles without purging lines led to flavor contamination and poor first-pour experiences
- Energy Inefficiency: Manual temperature control resulted in frequent overcooling, driving up electricity costs
- Inconsistent Serving Temperature: Temperature fluctuations affected beer quality and customer satisfaction
- Operational Complexity: Managing multiple taps, temperature zones, and cleaning cycles required constant attention
The Opportunity: I saw potential to leverage IoT sensors, machine learning, and thoughtful UX design to create an intelligent system that could automate these processes while providing precise control when needed. This represented a chance to solve real problems while expanding my technical skillset beyond traditional UX work.
Design Process
Taking on this project meant wearing multiple hats - UX researcher, systems designer, hardware prototyper, and developer. I followed a user-centered design approach while learning new technical skills along the way.
Discovery & Research
Understanding the Problem Space
Conducted user research with fellow homebrewers and small brewery operators to validate pain points. Analyzed existing keezer control solutions and identified gaps in current offerings. Key insight: most solutions focused on basic temperature control but ignored the dispensing UX.
Ideation & Conceptualization
Defining the Vision
Sketched multiple approaches ranging from simple automation to full ML integration. Decided on an approach that would provide immediate value (automated line purging) while building toward advanced features (predictive cooling optimization).
Hardware Prototyping
Learning Electronics from Scratch
Despite limited electronics experience, I dove into learning about Raspberry Pi GPIO, solenoid control, temperature sensors, and power management. Built multiple breadboard prototypes to test individual components before integration.
Software Architecture
Designing the System
Created a modular software architecture that separated concerns: hardware control, data collection, ML processing, and UI layers. This approach allowed me to iterate on individual components without breaking the entire system.
UI/UX Design
Crafting the User Experience
Designed a touchscreen interface optimized for the brewing environment - large touch targets, high contrast, and workflows that work with wet hands. Created both local and remote interfaces for different use cases.
Integration & Testing
Bringing It All Together
Integrated all components and began extensive testing in real brewing scenarios. Discovered issues with sensor reliability, timing logic, and edge cases that only appeared during actual use.
The Solution
SingleTap emerged as an intelligent keezer control system that addresses each pain point through a combination of smart hardware, machine learning algorithms, and thoughtful UX design.
Smart Beer Selection
Automated line purging and solenoid control ensures clean pours every time. System remembers last dispensed beer and purges lines when switching styles.
ML-Powered Cooling
Machine learning algorithm analyzes ambient temperature, weather forecasts, and historical data to optimize cooling cycles and reduce energy consumption.
Intuitive Interface
Large touchscreen interface designed for brewery environments, plus remote web dashboard for monitoring and configuration.
Data-Driven Insights
Comprehensive logging and analytics provide insights into usage patterns, energy efficiency, and system performance.
System Architecture
Raspberry Pi 4
Temperature Sensors
Flow Sensors
Solenoid Controls
11.9" Touchscreen
Weather API
# Core temperature control logic with ML optimization
def optimize_cooling_cycle():
current_temp = read_temperature_sensors()
ambient_temp = get_ambient_temperature()
weather_forecast = fetch_weather_data()
# ML model predicts optimal cooling strategy
prediction = ml_model.predict([
current_temp, ambient_temp,
weather_forecast['temp_trend'],
time_of_day, historical_efficiency
])
if current_temp > setpoint + 2:
engage_cooling(prediction['strategy'])
log_action('cooling_engaged', prediction)
Challenges & Learning
Building SingleTap as a solo practitioner meant constantly learning new skills while solving complex technical challenges. Each obstacle became a learning opportunity.
🔧 Hardware Integration Complexity
Challenge: Managing multiple voltage levels (3.3V sensors, 5V logic, 12V solenoids, 110V AC)
safely and reliably.
Solution: Extensive research into proper isolation techniques, MOSFET drivers,
and solid-state relays. Multiple prototype iterations to ensure safe operation.
📚 Limited Programming Background
Challenge: Implementing real-time sensor monitoring, ML algorithms, and concurrent
processing with minimal coding experience.
Solution: Structured learning approach - built simple prototypes first, leveraged
existing libraries, and gradually increased complexity. Embraced failure as learning.
🎯 Balancing Automation vs. Control
Challenge: Users wanted automation benefits but feared losing control over their brewing process.
Solution: Designed progressive disclosure - system provides intelligent defaults but
allows manual override at any point. Transparency in automated decisions builds trust.
⚡ Real-time Performance Requirements
Challenge: Ensuring responsive UI while running background ML processing, sensor monitoring,
and hardware control.
Solution: Implemented proper threading, async operations, and efficient data structures.
Learned about real-time systems constraints the hard way.
Results & Impact
After months of development and testing, SingleTap has delivered measurable improvements in both user experience and operational efficiency.
34%
Reduction in Energy Consumption
+
Improved Cooling Cycles
±0.5°F
Temperature Control Accuracy
Variable
Automated Sleep Timer
Key Achievements
- Seamless User Experience: Dispensing beer now requires a single tap selection - all purging and line management happens automatically
- Predictive Cooling: ML model successfully predicts cooling needs based on weather patterns, reducing compressor cycles by 40%
- Energy Optimization: Smart fan control and weather-aware cooling strategies significantly reduced electricity costs
- Data-Driven Insights: Comprehensive logging reveals usage patterns and optimization opportunities
Reflection & Next Steps
This project pushed me far beyond my comfort zone as a UX practitioner, forcing me to learn hardware design, embedded programming, and machine learning. The experience reinforced my belief that the best solutions come from understanding problems holistically.
What I Learned
- Systems Thinking: UX extends beyond screens - the entire interaction ecosystem matters
- Technical Empathy: Understanding implementation constraints makes me a better designer
- Iterative Problem Solving: Complex systems require building up from simple, working components
- User Validation: Even technical projects benefit enormously from user research and testing
Future Enhancements
- Advanced Analytics: Deeper insights into beer consumption patterns and preferences
- Brewery Integration: Connect with brewing software for inventory management
- Community Features: Share recipes and optimization strategies with other brewers
- Energy Trading: Dynamic cooling based on real-time electricity pricing
Design Philosophy
SingleTap embodies my belief that technology should enhance rather than complicate human experiences. By focusing on real user problems and leveraging appropriate technology, we can create solutions that feel magical while remaining grounded in practical utility.